Self-Evolving Digital Twin Ecosystems for Early Detection and Precision Management of Breast, Lung, Colorectal, and Ovarian Cancers
Year: 2026
Type: article
Abstract: Background: The convergence of multi-omics profiling, radiomics, and advanced machine learning has opened new frontiers in precision oncology.Despite this progress, existing computational frameworks remain static, failing to incorporate evolving patient data or simulate treatment outcomes dynamically.A self-evolving digital twin ecosystem offers a solution by continuously synchronizing patient-specific virtual models with real-world clinical trajectories.Objective: To develop and validate a self-evolving digital twin ecosystem integrating clinical, genomic, transcriptomic, proteomic, radiomic, and pathological data for early cancer detection, disease progression prediction, and personalized treatment optimization across breast, lung, colorectal, and ovarian cancers.Methods: A retrospective multi-center cohort of 400 patients (breast n=120, lung n=100, colorectal n=100, ovarian n=80; age range 25-75 years) was assembled.A six-layer digital twin framework was constructed, incorporating a cross-modal transformer foundation model alongside Random Forest, XGBoost, and Deep Neural Network comparators.Key performance metrics included accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).Virtual clinical trial simulations and feature importance analyses were performed.Results: The foundation model achieved superior performance across all metrics (accuracy 0.921, AUC 0.947) compared with Random Forest (0.812/0.847),XGBoost (0.841/0.879), and Deep Neural Network (0.873/0.906).Tumor stage, TP53 mutation, and EGFR mutation emerged as the most predictive features.Risk stratification identified 148 (37.0%) low-risk, 153 (38.3%) moderate-risk, and 99 (24.8%)high-risk patients.Virtual trial simulation demonstrated a 23.4% improvement in projected progression-free survival for the high-risk subgroup.Conclusion: The self-evolving digital twin ecosystem significantly outperforms conventional machine learning approaches and provides a clinically actionable, adaptive platform for real-time precision oncology decision support across multiple cancer types.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7221528048